Lamini
Reduce hallucinations and improve LLM accuracy effectively
Target Audience
- Data scientists
- Business analysts
- Enterprise IT teams
- Customer service managers
Hashtags
Overview
Lamini is an enterprise-level platform designed to enhance the accuracy of large language models (LLMs) while significantly reducing errors known as hallucinations. It enables businesses to create specialized mini-agents that can process and analyze data with remarkable precision. This tool is crucial for organizations that rely on accurate data handling and want to maintain data privacy.
Key Features
High Accuracy
Achieve up to 95% accuracy on tuned models
Memory RAG
Utilize embed-time compute for smarter, efficient retrieval
Classifier Toolkit
Automate data classification tasks effortlessly
On-Premise Deployment
Maintain data privacy with secure local installations
Function Calling
Connect seamlessly to external APIs and tools
Use Cases
Text-to-SQL for business analysis
Automate manual classification tasks
Build intelligent customer service agents
Function calling for productivity boost
Code assistance for niche programming
Pros & Cons
Pros
- Significantly reduces LLM hallucinations
- High accuracy rates for complex tasks
- Flexible deployment options, including air-gapped environments
- User-friendly tools for automating classification tasks
Cons
- Primarily targeted at enterprise-level users
- May require technical knowledge to implement fully
Pricing Plans
Standard Plan
One-timeFeatures
- Free credits to start
- Access to Memory RAG
- Use of Classifier Toolkit
Pricing may have changed
For the most up-to-date pricing information, please visit the official website.
Visit websiteFrequently Asked Questions
What is Memory RAG?
Memory RAG is a feature that enhances the accuracy of RAG agents using embed-time compute.
How does Lamini ensure data privacy?
Lamini can be deployed in secure environments, including on-premise setups, to ensure data privacy.
Who can benefit from using Lamini?
Lamini is designed for enterprises that need high accuracy in LLM applications and want to automate data processes.
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